docs: def results_class_wise_average_metrics(self):
          """Class-wise averaged metrics
  
          Returns
          -------
          dict
              results in a dictionary format
  
          """
  
          class_wise_results = self.results_class_wise_metrics()
  
          class_wise_eer = []
          class_wise_fmeasure = []
          class_wise_precision = []
          class_wise_recall = []
  
          for class_label in class_wise_results:
              if class_wise_results[class_label]['eer']['eer'] is not None:
                  class_wise_eer.append(class_wise_results[class_label]['eer']['eer'])
  
              if class_wise_results[class_label]['f_measure']['f_measure'] is not None:
                  class_wise_fmeasure.append(class_wise_results[class_label]['f_measure']['f_measure'])
                  class_wise_precision.append(class_wise_results[class_label]['f_measure']['precision'])
                  class_wise_recall.append(class_wise_results[class_label]['f_measure']['recall'])
  
          if class_wise_eer:
              eer = float(numpy.nanmean(class_wise_eer))
  
          else:
              eer = None
  
          if class_wise_fmeasure:
              f_measure = float(numpy.nanmean(class_wise_fmeasure))
  
          else:
              f_measure = None
  
          if class_wise_precision:
              precision = float(numpy.nanmean(class_wise_precision))
  
          else:
              precision = None
  
          if class_wise_recall:
              recall = float(numpy.nanmean(class_wise_recall))
  
          else:
              recall = None
  
          return {
              'eer': {
                  'eer': eer
              },
              'f_measure': {
                  'f_measure': f_measure,
                  'precision': precision,
                  'recall': recall,
              }
          }